Introduction

This analysis explores relationships between indicators across countries such as percentage of agricultural land, CO2 emissions per capita, and size of surface area using World Bank data. It is divided into two main parts, with this script focusing on the second question. For further observation of the first question, refer to the file ‘Analysis/agriculture.Rmd’.

1. Is there a relationship between the percentage of agricultural land and CO2 emissions per capita across countries?

2. Does the size of the surface area of the country play a role?

World Bank Indicators
Variable Indicator Name Definition
AG.LND.AGRI.ZS Agricultural land (% of land area) Agricultural land refers to the share of land area that is arable, under permanent crops, and under permanent pastures. Arable land includes land defined by the FAO as land under temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded. Land under permanent crops is land cultivated with crops that occupy the land for long periods and need not be replanted after each harvest, such as cocoa, coffee, and rubber. This category includes land under flowering shrubs, fruit trees, nut trees, and vines, but excludes land under trees grown for wood or timber. Permanent pasture is land used for five or more years for forage, including natural and cultivated crops.
AG.SRF.TOTL.K2 Surface area (sq. km) Surface area is a country’s total area, including areas under inland bodies of water and some coastal waterways.
EN.GHG.CO2.MT.CE.AR5 Carbon dioxide (CO2) emissions (total) excluding LULUCF (Mt CO2e) A measure of annual emissions of carbon dioxide (CO2), one of the six Kyoto greenhouse gases (GHG), from the building sector (subsector of the energy sector) including IPCC 2006 codes 1.A.4 Residential and other sectors, 1.A.5 Non-Specified. The measure is standardized to carbon dioxide equivalent values using the Global Warming Potential (GWP) factors of IPCC’s 5th Assessment Report (AR5).
SP.POP.TOTL Population, total Total population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. The values shown are midyear estimates.

Agenda

2.) Influence of surface area on previous relationship

2.1.) Radar chart of interested main variables

2.2.) Point-line plot of surface area with faceted countries

2.3.) Bar plot of absolute changes with faceted countries; changing countries

2.4.) Point-line plot of relative changes with faceted countries; changing countries

2.5.) Boxplot of surface area

2.6.) Scatter plot of interested variables with color scale and with faceted grouping

2.7.) Point-line plot of CO2 emissions with faceted grouping and color scale

2.8.) Point-line plot of interested variables with faceted grouping; normalized

2.9.) Point-line plot of interested variables with faceted countries; on per basis

2.10.) Point-line plot of interested variables with faceted grouping; on per basis

3.) Summary and Outlook

2. Influence of surface area on previous relationship

One further aspect that might change the non-relationship recorded in the ‘agriculture.Rmd’ file is the introduction of another variable to take into account, namely the countries’ surface areas.

2.1. Radar chart of interested main variables

Starting with the initial comparison between the variables within the ranges of the data observed, we get the following first overview.

##     Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
##      180   243610   796100  2461119  1285220 17098250

2.2. Point-line plot of surface area with faceted countries


As we can see, there are several countries with no changes in surface area throughout the interested time span at all. Therefore, before heading forward, we first want to zoom in a little closer on those with changes.

## Anzahl der Länder ohne Veränderungen:  10

2.3. Bar plot of absolute changes with faceted countries; changing countries


For the vast majority of the countries, the changes can be classified as under 1000 square kilometers over the whole time span. Similar insights can be derived when looking at the relative changes.

2.4. Point-line plot of relative changes with faceted countries; changing countries


For each country, even those with changes throughout the time span, there are at most marginal changes of two percent in surface area.

2.5. Boxplot of surface area

To finally confirm those claims, we take a look at the scatter decomposition.

## Streuung zwischen den Ländern:  1.783129e+13


In conclusion, we recognize that the changes in surface area are negligible over time. Therefore, we drop our focus on the development over time considering this variable when moving on. More interesting might be shifting the perspective towards whether the absolute amount of surface area has any influence on the relationship between agricultural land and CO2 emissions for the observed countries.

2.6. Scatter plot of interested variables with color scale and with faceted grouping

For this exploration, we want to distinguish our countries into the following groups:
We see there is no direct influence obvious through the grouping of the data. Let’s dig deeper by looking at the time-specific distribution.

2.7. Point-line plot of CO2 emissions with faceted grouping and color scale


The biggest anomalies regarding the CO2 emissions with the percentage of agricultural land in mind seem to be the moderate and very large surface area countries. Here on one hand, we can detect comparably high percentages in agricultural land for the moderate area countries, but those do not transfer themselves to any obvious differences in the CO2 emissions compared to the other groups. On the other hand, the very large countries stand out by having the supposedly expectable highest CO2 emissions among all groups. Marginal differences appear between the development over time, as the very large area countries are constant over the two decade timespan, while the other groups have slightly increasing trends.

2.8. Point-line plot of interested variables with faceted grouping; normalized

If we finally pivot back to our normalized comparison we did earlier, we can do the same now with our grouped data according to the surface area categories.


We cannot identify any obvious connection between the CO2 emissions per capita and the percentage of agricultural land even with the interested countries categorized by surface area.

2.9. Point-line plot of interested variables with faceted countries; on per basis

We recognize some similar developments over the years for the majority of countries between the CO2 emissions in Gigatonnes per square kilometer of agricultural land on one hand and per square kilometer of the country’s surface area on the other hand.
Nevertheless, two appearing anomalies can be observed: Firstly, there are widely differing courses of co2 emissions between the per agricultural land and per surface area developments for some countries, namely being Aruba, Qatar, Finland, and Peru. Although the developments for the other countries are more or less differing as well, for these four countries the courses are by far the most. Categorized by very small (2x), small and large surface areas, conclusions stemming from the classification remain difficult. Conspicuous though, that all of these countries have noticeably low per surface area values with basically negligible increase, i.e. horizontal progression.
Secondly, the few countries with mainly decreasing values catch the eye, namely Czechia, Finland, the United Kingdom, Afghanistan, and the United States. Here we have very small, small (2x), middle and very large representations, again meaning there are no obvious differences between the classifications.

2.10. Point-line plot of interested variables with faceted grouping; on per basis

Making it more representative by using the grouping for the faceting and investigating the relationship within the groups, shows the following.

For all of the classifications, the robust-linear relationships are slightly positive and progressing approximately parallel for both, the CO2 emissions in Gigatonnes per square kilometer of agricultural land as well as per square kilometer of surface area. However, the free y-scaling shows interesting deviations in the dimensions. While the very large countries also have the by far highest values for CO2 emissions, the other four categories are swapped, i.e. the values decrease with increasing surface area category. Especially the large category steps out of line with considerably fewer emissions than the others.